import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A						=			1_6
A						=			3_2


def 	lowerCamelCase					(	UpperCamelCase     :				Accelerator			,       UpperCamelCase     :				int = 16			,       UpperCamelCase     :				str = "bert-base-cased"							)   ->    Any:
		_lowerCamelCase				   =  AutoTokenizer.from_pretrained(UpperCamelCase							)
		_lowerCamelCase				   =  load_dataset('glue'			,       'mrpc'							)

		def tokenize_function(UpperCamelCase     :				int							):
				# max_length=None => use the model max length (it's actually the default)
				_lowerCamelCase				   =  tokenizer(examples['sentence1']			,       examples['sentence2']			,       truncation=UpperCamelCase			,       max_length=UpperCamelCase							)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		_lowerCamelCase				   =  datasets.map(
		    UpperCamelCase			,       batched=UpperCamelCase			,       remove_columns=['idx', 'sentence1', 'sentence2']			,       load_from_cache_file=UpperCamelCase							)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		_lowerCamelCase				   =  tokenized_datasets.rename_column('label'			,       'labels'							)

		def collate_fn(UpperCamelCase     :				Dict							):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(UpperCamelCase			,       padding='max_length'			,       max_length=1_28			,       return_tensors='pt'							)
				return tokenizer.pad(UpperCamelCase			,       padding='longest'			,       return_tensors='pt'							)

		# Instantiate dataloaders.
		_lowerCamelCase				   =  DataLoader(
		    tokenized_datasets['train']			,       shuffle=UpperCamelCase			,       collate_fn=UpperCamelCase			,       batch_size=UpperCamelCase							)
		_lowerCamelCase				   =  DataLoader(
		    tokenized_datasets['validation']			,       shuffle=UpperCamelCase			,       collate_fn=UpperCamelCase			,       batch_size=UpperCamelCase							)

		return train_dataloader, eval_dataloader


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple			,       UpperCamelCase     :				str			,       UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				List[Any]							)   ->    Union[str, Any]:
		model.eval()
		_lowerCamelCase				   =  0
		for step, batch in enumerate(UpperCamelCase							):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device							)
				with torch.no_grad():
						_lowerCamelCase				   =  model(**UpperCamelCase							)
				_lowerCamelCase				   =  outputs.logits.argmax(dim=-1							)
				# It is slightly faster to call this once, than multiple times
				_lowerCamelCase   ,							_lowerCamelCase				   =  accelerator.gather(
				    (predictions, batch['labels'])							)  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(UpperCamelCase							) - 1:
								_lowerCamelCase				   =  predictions[: len(eval_dataloader.dataset							) - samples_seen]
								_lowerCamelCase				   =  references[: len(eval_dataloader.dataset							) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=UpperCamelCase			,       references=UpperCamelCase			,       )

		_lowerCamelCase				   =  metric.compute()
		return eval_metric["accuracy"]


def 	lowerCamelCase					(	UpperCamelCase     :				Any			,       UpperCamelCase     :				Dict							)   ->    Union[str, Any]:
		# Initialize accelerator
		_lowerCamelCase				   =  Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_lowerCamelCase				   =  config['lr']
		_lowerCamelCase				   =  int(config['num_epochs']							)
		_lowerCamelCase				   =  int(config['seed']							)
		_lowerCamelCase				   =  int(config['batch_size']							)
		_lowerCamelCase				   =  args.model_name_or_path

		set_seed(UpperCamelCase							)
		_lowerCamelCase   ,							_lowerCamelCase				   =  get_dataloaders(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_lowerCamelCase				   =  AutoModelForSequenceClassification.from_pretrained(UpperCamelCase			,       return_dict=UpperCamelCase							)

		# Instantiate optimizer
		_lowerCamelCase				   =  (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		_lowerCamelCase				   =  optimizer_cls(params=model.parameters()			,       lr=UpperCamelCase							)

		if accelerator.state.deepspeed_plugin is not None:
				_lowerCamelCase				   =  accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				_lowerCamelCase				   =  1
		_lowerCamelCase				   =  (len(UpperCamelCase							) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				_lowerCamelCase				   =  get_linear_schedule_with_warmup(
				    optimizer=UpperCamelCase			,       num_warmup_steps=0			,       num_training_steps=UpperCamelCase			,       )
		else:
				_lowerCamelCase				   =  DummyScheduler(UpperCamelCase			,       total_num_steps=UpperCamelCase			,       warmup_num_steps=0							)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  accelerator.prepare(
		    UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)

		# We need to keep track of how many total steps we have iterated over
		_lowerCamelCase				   =  0
		# We also need to keep track of the stating epoch so files are named properly
		_lowerCamelCase				   =  0
		_lowerCamelCase				   =  evaluate.load('glue'			,       'mrpc'							)
		_lowerCamelCase				   =  num_epochs

		if args.partial_train_epoch is not None:
				_lowerCamelCase				   =  args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint							)
				_lowerCamelCase				   =  args.resume_from_checkpoint.split('epoch_'							)[1]
				_lowerCamelCase				   =  ''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				_lowerCamelCase				   =  int(UpperCamelCase							) + 1
				_lowerCamelCase				   =  evaluation_loop(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
				accelerator.print('resumed checkpoint performance:'			,       UpperCamelCase							)
				accelerator.print('resumed checkpoint\'s scheduler\'s lr:'			,       lr_scheduler.get_lr()[0]							)
				accelerator.print('resumed optimizers\'s lr:'			,       optimizer.param_groups[0]['lr']							)
				with open(os.path.join(args.output_dir			,       F"""state_{starting_epoch-1}.json"""							)			,       'r'							) as f:
						_lowerCamelCase				   =  json.load(UpperCamelCase							)
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		_lowerCamelCase				   =  {}
		for epoch in range(UpperCamelCase			,       UpperCamelCase							):
				model.train()
				for step, batch in enumerate(UpperCamelCase							):
						_lowerCamelCase				   =  model(**UpperCamelCase							)
						_lowerCamelCase				   =  outputs.loss
						_lowerCamelCase				   =  loss / gradient_accumulation_steps
						accelerator.backward(UpperCamelCase							)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				_lowerCamelCase				   =  F"""epoch_{epoch}"""
				_lowerCamelCase				   =  os.path.join(args.output_dir			,       UpperCamelCase							)
				accelerator.save_state(UpperCamelCase							)
				_lowerCamelCase				   =  evaluation_loop(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
				_lowerCamelCase				   =  accuracy
				_lowerCamelCase				   =  lr_scheduler.get_lr()[0]
				_lowerCamelCase				   =  optimizer.param_groups[0]['lr']
				_lowerCamelCase				   =  epoch
				_lowerCamelCase				   =  overall_step
				accelerator.print(F"""epoch {epoch}:"""			,       UpperCamelCase							)

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,       F"""state_{epoch}.json"""							)			,       'w'							) as f:
								json.dump(UpperCamelCase			,       UpperCamelCase							)


def 	lowerCamelCase					(	)   ->    Tuple:
		_lowerCamelCase				   =  argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'							)
		parser.add_argument(
		    '--model_name_or_path'			,       type=UpperCamelCase			,       default='bert-base-cased'			,       help='Path to pretrained model or model identifier from huggingface.co/models.'			,       required=UpperCamelCase			,       )
		parser.add_argument(
		    '--output_dir'			,       type=UpperCamelCase			,       default='.'			,       help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'			,       )
		parser.add_argument(
		    '--resume_from_checkpoint'			,       type=UpperCamelCase			,       default=UpperCamelCase			,       help='If the training should continue from a checkpoint folder.'			,       )
		parser.add_argument(
		    '--partial_train_epoch'			,       type=UpperCamelCase			,       default=UpperCamelCase			,       help='If passed, the training will stop after this number of epochs.'			,       )
		parser.add_argument(
		    '--num_epochs'			,       type=UpperCamelCase			,       default=2			,       help='Number of train epochs.'			,       )
		_lowerCamelCase				   =  parser.parse_args()
		_lowerCamelCase				   =  {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

		training_function(UpperCamelCase			,       UpperCamelCase							)


if __name__ == "__main__":
				main()
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A						=			input('Enter image url: ').strip()
				print(F'''Downloading image from {url} ...''')
				A						=			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A						=			soup.find('meta', {'property': 'og:image'})['content']
				A						=			requests.get(image_url).content
				A						=			F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
				with open(file_name, 'wb') as fp:
								fp.write(image_data)
				print(F'''Done. Image saved to disk as {file_name}.''')